The 2016 RWTH Keyword Search System for Low-Resource Languages

2017 ◽  
pp. 719-730 ◽  
Author(s):  
Pavel Golik ◽  
Zoltán Tüske ◽  
Kazuki Irie ◽  
Eugen Beck ◽  
Ralf Schlüter ◽  
...  
Keyword(s):  
Keyword Search ◽  
Search System ◽  
Low Resource

scholarly journals Improved Ability Multi Keyword Search System on Cloud Web Server

2019 ◽  
Vol 8 (6) ◽  
pp. 2421-2425
Keyword(s):  
Mobile Devices ◽  
Keyword Search ◽  
Cell Phones ◽  
Web Server ◽  
Calculation Procedure ◽  
Search System ◽  
Mobility Devices ◽  
Major Stage

The movement of clients from desktop to mobility devices, made a major stage in the portable trade. All the up and coming advancements, parts, delicate products are very composed by the portable. As versatility is unavoidable prerequisite by the clients, the outline of programming with less battery utilization are generally invited. The calculation procedure is relative to the battery utilization. The calculation at the cell phones genuinely influences the series of the portable. Hence making the calculation at the cloud has an awesome arrangement in diminishing the battery utilization. The delegate calculation inquiry is a productive approach to safeguard the battery of the mobile devices. Indeed, even the encryption/unscrambling of records takes control so proposing IOPE for scrambling the document which is a basic plan

Low-resource open vocabulary keyword search using point process models

2014 ◽  
Author(s):  
Chunxi Liu ◽  
Aren Jansen ◽  
Guoguo Chen ◽  
Keith Kintzley ◽  
Jan Trmal ◽  
...  
Keyword(s):  
Point Process ◽  
Keyword Search ◽  
Process Models ◽  
Low Resource ◽  
Point Process Models

A Novel classification framework for the Thirukkural for building an efficient search system

2021 ◽  
pp. 1-12
Author(s):  
Anita Ramalingam ◽  
Subalalitha Chinnaudayar Navaneethakrishnan
Keyword(s):  
Keyword Search ◽  
Performance Comparison ◽  
Support Vector ◽  
Comparison Analysis ◽  
Average Precision ◽  
Search System ◽  
Didactic Literature ◽  
Classification Framework ◽  
Bayes Algorithm ◽  
Google Search

Thirukkural, a Tamil classic literature, which was written in 300 BCE is a didactic literature. Though Thirukkural comprises 1330 couplets which are organized into three sections and 133 chapters, in order to retrieve meaningful Thirukkural for a given query in search systems, a better organization of the Thirukkural is needed. This paper lays such a foundation by classifying the Thirukkural into ten new categories called superclasses that is helpful for building a better Information Retrieval (IR) system. The classifier is trained using Multinomial Naïve Bayes algorithm. Each superclass is further classified into two subcategories based on the didactic information. The proposed classification framework is evaluated using precision, recall and F-score metrics and achieved an overall F-score of 82.33% and a comparison analysis has been done with the Support Vector Machine, Logistic Regression and Random Forest algorithms. An IR system is built on top of the proposed system and the performance comparison has been done with the Google search and a locally built keyword search. The proposed classification framework has achieved a mean average precision score of 89%, whereas the Google search and keyword search have yielded 59% and 68% respectively.

Named Entity Based Ranking with Term Proximity for XML Retrieval

2018 ◽  
Vol 8 (2) ◽  
pp. 57-77 ◽  
Author(s):  
Abubakar Roko ◽  
Shyamala Doraisamy ◽  
Azreen Azman ◽  
Azrul Hazri Jantan
Keyword(s):  
Keyword Search ◽  
Query Term ◽  
Search System ◽  
Keyword Query ◽  
Xml Retrieval ◽  
Named Entity ◽  
Data Content ◽  
Search Quality ◽  
The Impact ◽  
Confidence Value

In this article, an indexing scheme that includes the named entity category for each indexed term is proposed. Based on this, two methods are proposed, one to infer the semantics of an XML element based on its data content, called the confidence value of the element, and the second method computes the proximity scores of the query terms. The confidence value of an element is obtained based on the probability of a named entity category in the data content of the underlying XML element. The proximity score of the query terms measures the proximity and ordering of the query term within an XML element. The article then shows how a ranking function uses the confidence value of an XML element and proximity score to mitigate the impact of higher frequency terms and compute the relevance between a keyword query and an XML fragment. Finally, a keyword search system is introduced and experiments show that the proposed system outperforms existing approaches in terms of search quality and achieve a higher efficiency.

Sign in / Sign up

Export Citation Format

Share Document